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I looking for a way to efficiently apply a function to each row of data.table. Let's consider the following data table:
library(data.table) library(stringr) x <- data.table(a = c(1:3, 1), b = c('12 13', '14 15', '16 17', '18 19')) > x a b 1: 1 12 13 2: 2 14 15 3: 3 16 17 4: 1 18 19 Let's say I want to split each element of column b by space (thus yielding two rows for each row in the original data) and join the resulting data tables. For the example above, I need the following result:
a V1 1: 1 12 2: 1 13 3: 2 14 4: 2 15 5: 3 16 6: 3 17 7: 1 18 8: 1 19 The following would work if column a has only unique values:
x[, list(str_split(b, ' ')[[1]]), by = a] The following almost works (unless there are some identical rows in the original data table), but is ugly when x has many columns and copies column b to the result, which I would like to avoid.
> x[, list(str_split(b, ' ')[[1]]), by = list(a,b)] a b V1 1: 1 12 13 12 2: 1 12 13 13 3: 2 14 15 14 4: 2 14 15 15 5: 3 16 17 16 6: 3 16 17 17 7: 1 18 19 18 8: 1 18 19 19 What would be the most efficient and idiomatic way to solve this problem?
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Use apply() function when you wanted to update every row in pandas DataFrame by calling a custom function. In order to apply a function to every row, you should use axis=1 param to apply(). By applying a function to each row, we can create a new column by using the values from the row, updating the row e.t.c.
Other ways to add rows and columns Add a row or column to a table by typing in a cell just below the last row or to the right of the last column, by pasting data into a cell, or by inserting rows or columns between existing rows or columns.
How about :
x a b 1: 1 12 13 2: 2 14 15 3: 3 16 17 4: 1 18 19 x[,list(a=rep(a,each=2), V1=unlist(strsplit(b," ")))] a V1 1: 1 12 2: 1 13 3: 2 14 4: 2 15 5: 3 16 6: 3 17 7: 1 18 8: 1 19 Generalized solution given comment :
x[,{s=strsplit(b," ");list(a=rep(a,sapply(s,length)), V1=unlist(s))}] 
x[, .(a,strsplit(b,' ')), by=1:nrow(x)] by=nrow(x) is a simple way to force 1 row per by-group
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